Saturday, September 7, 2013

overview of the treatment of "information"

I'm trying to remember which Dembski critique was claiming that genetic algorithms are a dark art. And which was saying that genetic algorithms have a solid mathematical foundation in the work of Fisher. Is the work of Fisher a red herring for the fact that genetic algorithms have to manipulated into provide specifc sorts of answers?

The trick in genetic algorithms is to find schemes that do this mapping from a binary bit-string to an engineering design efficiently and elegantly, rather than by brute-force.... The genetic operators copy and modify the genotypes from one generation to the next.... Getting the right balance between mutation and selection is especially important.... Finally, the evolutionary parameters [such as population size and mutation rate] determine the general context for evolution and the quantitative details of how the genetic operators work.... Deciding the best values for these parameters in a given application remains a black art, driven more by blind intuition and communal tradition than by sound engineering principles.24

which I quoted here on this blog.

The first quote, I must've been thinking of the "eandsdembski" paper. Elsberry and Shallitt actually try to avoid the problematic claims about genetic algorithms and imply that we know that the amazing functional complexity we see in nature simply follows from the math:

Dembski asserts that \evolutionary algorithms" represent the mathematical underpinnings of Darwinian mechanisms of evolution [19, p. 180]. This claim is egregiously backward. A large body of scholarly work is completely ignored by Dembski in order to make this claim, including Ronald Fisher's 1930 book, The Genetical Theory of Natural Selection.[16] It is evolutionary computation which takes its underpinnings from the robust mathematical formulations which were worked out in the literature of evolutionary biology.

They draw a distinction between genetic algorithms and artificial life. They seem to be implying that none of the fine tuning done for genetic algorithms applies to evolutionary computing in artificial life, as it's general target of survival doesn't predispose it to solving particular, goal-directed problems (such as Schneider's ev program?).

Aside:
It occurs to me that the information going from the environment to the population in question should be represented as the logarithm of the decrease in probability of death before reproduction. Given all the bits of information being absorbed by a population about property X, what would the signal to noise ratio be?